Tesla’s Strategic Expansion into Global Markets: The Role of Artificial Intelligence in Electronic Vehicles and its Implications for HR Management

Abstract

The strategic international expansion of Tesla Inc. and the integration of Artificial Intelligence (AI) in its operations highlight the intersection of technology and global business strategies. Despite considerable progress, the adoption of AI is still relatively under-explored with respect to challenges such as ethical concerns and workforce adaptation. Hence, this study aims to explore the effect of AI on Tesla’s global market strategies and their consequences for Human Resource Management (HRM). The research examines how AI-driven innovations in autonomous driving, production processes, and HR practices improve Tesla’s competitive edge using secondary qualitative thematic analysis. The findings indicate that AI significantly enhances operational efficiency, decision-making, and employee engagement, however, it presents challenges related to ethics and continuous upskilling. The research, thus, contributes to the broader discourse on AI in international business and provides an avenue towards best practices in integrating AI in strategic expansion and HRM. The results are crucial for organisations seeking to use AI as a driver of global competitiveness.

Keywords: Artificial Intelligence (AI), Strategic Internationalisation, Human Resource Management (HRM), Tesla

Table of Contents

• Chapter 1: Introduction. 7
• 1.1        Introduction. 7
• 1.2        Background. 7
• 1.3        Problem Statement 9
• 1.4        Rationale for the Study. 10
• 1.5        Research Aim.. 10
• 1.6        Research Questions. 10
• 1.7        Research Objectives. 11
• 1.8        Research Significance. 11
• 1.9        Dissertation Outline. 11
• Chapter 2: Literature Review.. 13
• 2.1        Introduction. 13
• 2.2        Conceptual Framework. 13
• 2.2.1        Key Concepts. 13
• 2.2.2        Intersection of AI, Strategic Internationalisation, and HRM… 14
• 2.3        Theoretical Framework. 15
• 2.3.1        Resource-Based View (RBV) 15
• 2.3.2        Technology Acceptance Model (TAM) 16
• 2.3.3        Porter’s Five Forces. 17
• 2.4        AI in the Automotive Industry. 18
• 2.5        Tesla’s Strategic Internationalisation. 19
• 2.6        AI and HR Management 20
• 2.7        Link between AI, Strategic Internationalisation, and HRM… 22
• 2.8        Literature Gap. 23
• 2.9        Chapter Summary. 24
• Chapter 3: Methodology. 25
• 3.1        Introduction. 25
• 3.2        Philosophical Underpinnings. 25
• 3.3        Research Design. 26
• 3.4        Data Collection Methods. 27
• 3.5        Data Analysis. 27
• 3.6        Strengths and Limitations of Methodology. 28
• 3.7        Ethical Considerations. 28
• 3.8        Chapter Summary. 29
• Chapter 4: Analysis and Discussion. 30
• 4.1        Introduction. 30
• 4.2        Thematic Analysis. 30
• 4.2.1        Role of AI in Strategic Internationalisation of Tesla. 30
• 4.2.2        Impact of AI Technology on HRM in Tesla. 31
• 4.2.3        Opportunities and Challenges in Adopting AI in Tesla’s HRM… 33
• 4.3        Theoretical Implications. 35
• 4.4        Chapter Summary. 36
• Chapter 5: Conclusion and Recommendations. 37
• 5.1        Theoretical Implications. 37
• 5.2        Practical Implications. 38
• 5.3        Limitations of the Study. 39
• 5.4        Recommendations for Future Research. 39
• 5.5        Final Remarks. 40
• References. 41

1.      Chapter 1: Introduction

1.1    Introduction

Chapter 1 provides the background information, problem statement, research significance, and rationale for study followed by the research aim, questions, and objectives. It generally provides the context around the impacts that AI technology have towards strategic internationalisation and HRM practices of Tesla.

1.2    Background

Tesla, Inc., founded in 2003 by Martin Eberhard and Marc Tarpenning, rapidly evolved from a niche Electric Vehicle (EV) manufacturer into the champion of innovation in automobiles and renewable energy globally. “To accelerate the world’s transition to sustainable energy” is the mission driving Tesla’s strategic initiatives, technological advancements, product development, and growth in the market (Thomas and Maine, 2019). The products in its portfolio range from electric vehicles to energy storage systems, solar products, and AI-driven technologies. 

Saxena, and Vibhandik (2021) highlighted that the journey from its inception to becoming one of the global market leaders has been marked by strategic internationalisation. First, it targeted American markets, as evidenced by the launch of the Tesla Roadster in 2008. However, after realizing that there was more sustainable transport held across the globe, the company actively ventured to expand into Europe and further into Asia. Probably among the most significant steps toward internationalisation were initiatives of Gigafactory Shanghai in China and that of Gigafactory Berlin-Brandenburg in Germany (Han, 2021). Such facilities maximise production capacity while providing Tesla with a number of benefits to serve regional markets where the complexities of supply chains and import tariffs are low.

Du and Li (2021) pointed out that Artificial Intelligence (AI) is currently the most central part of Tesla’s strategy, particularly in the development of EV technology and operational efficiency. Among the most prominent AI pursuits of Tesla is autonomous driving technology, also referred to as Full Self-Driving (FSD). Machine learning algorithms along with neural networks use large amounts of data to improve vehicle safety and driving experience (Akakpoet al., 2019). Tesla is interested in AI beyond autonomous driving and into its production lines, customer service, and energy management products.

The AI applications of Tesla are illustrated in the Autopilot and Full Self-Driving features as mentioned by McCain (2019). In this package, Autopilot enhances traffic-aware cruise control, lane keeping, and self-parking. According to Shao, Wang and Yang (2021), a full Self-Driving Package focuses on achieving Level 5 autonomy, where the vehicle can self-drive without interference from a driver. These AI-driven features not only enhance user experience but also position Tesla as a leader in this very competitive EV market as indicated in Figure 2. To keep improving these systems, there is an infrastructure for collecting data so that all Tesla vehicles learn and improve the algorithms over time.

Liu (2021) found that the impact of AI in Tesla’s activities is huge in the sphere of Human Resource Management (HRM). Adoption of AI in Human Resource (HR) processes can considerably improve recruitment, trainings, and employee engagement. Such AI-driven tools can simplify the process of recruitment by analyzing tons of resumes, finding the best candidates with pre-defined criteria, and even predicting candidate success within an organisation (Levchenko and Yanchuk, 2024). AI can also make individual training programs more efficient by pacing and personalizing them according to needs, hence developing and retaining better employees. However, Matthews et al. (2020) argued that some of the challenges exist in the implementation of AI in human resource management. Ensuring privacy, ethics, and no bias of AI algorithms has to be observed to ensure fairness and transparency needed in HR practices. Moreover, AI is changing so rapidly that workers have to upskill constantly to be competitive. On the other hand, there exist various opportunities of AI in respect of better decision-making, operational efficiency, and innovation. Thus, Tesla’s strategic internationalisation is also linked with its ability in AI implementation. Its capability to leverage AI in manufacturing, supply chain management, and customer service enhances the company’s global operational efficiency.

1.3    Problem Statement

Even though Tesla is successful and growing rapidly, there exist huge challenges and opportunities to integrate AI in its strategic internationalisation and human resource management. They include, but are not limited to, ethical concerns about AI algorithms, privacy issues, and possible biases. Another challenge is the incredible pace of AI development; hence, there is a need to constantly upgrade and adaptation within the workforce to sustain competitiveness. These challenges have to be addressed in order to enhance operational efficiency in the global market. Therefore, it is important to understand how AI in Tesla affects its expansion in the global market and its HR practices.

1.4    Rationale for the Study

The research is motivated by the increasing importance of AI in the automotive sector and its potential implications on corporate strategies worldwide. In many previous studies, Tesla has been a discussion topic due to its technological developments and market tactics. This research will give comprehensive results on technology, a growth agenda, and human capital administration by the analysis of strategic development at Tesla and the role of AI. The attempt to understand how AI is driving Tesla’s global expansion and its HR practices will provide meaningful insights for other companies looking to integrate AI in their own strategy. Additionally, this research will also contribute to the wider academic discourse in areas like AI, international business, and human resource management. The study also provides some practical implications involving operational efficiency in accordance with the changes in the global marketplace.

1.5    Research Aim

This research specifically aims to determine the impact of AI in shaping Tesla’s strategic internationalisation and its implications for HRM. The main objective is to identify the impact that the AI advances have on Tesla’s HR and global expansion.

1.6    Research Questions

1. How does AI help in Tesla’s strategic moves toward the international markets?
2. How do key areas of AI influence the practices of HR management at Tesla?

• What are the opportunities and threats that Tesla encounters in applying AI in its HRM strategies?

1.7    Research Objectives

1. To analyse the impact of AI in improving the company’s portfolio and its competitiveness in the global markets.
2. To understand how the current AI technology impacts HRM in Tesla, including recruitment, training, and employee engagement.
3. To examine the general obstacles and prospects for adopting AI into Tesla’s HR plans and processes.

1.8    Research Significance

The research significance lies in its potential to provide valuable insights into how AI has influenced the strategic internationalisation and HR management practices of Tesla. Focusing on how AI could help leverage the competitiveness and operational efficiency of Tesla at an international level, this research explains best practices and potential pitfalls of the integration of AI with implications. The findings are relevant not only to Tesla but also to many organisations that integrate AI into their international expansion and HR strategies. Moreover, the research may add to the broader discourse on the ethical concerns, challenges, and opportunities posed by AI in corporate life. Furthermore, this research is timely, relevant, and interesting due to rapid growth of AI technology and increased reliance on AI for strategic decision-making in a business. Under the dual pressures of technological evolution and globalisation, it has become essential for companies worldwide to understand the role of AI in internationalisation and human resource management in order to be competitive.

1.9    Dissertation Outline

This dissertation consists of five chapters. Chapter 1 presents an introduction to the research topic, including an explanation of the background information and the problem statement, research significance, and aims. Chapter 2: Literature Review will cover existing research related to AI, strategic internationalisation, and human resource management. A description of the methodology adopted for the research design is given in Chapter 3, together with data collection methods and analysis processes. Chapter 4 deals with the analysis and discussion of the findings regarding how AI has an impact on Tesla in relation to global expansion and HR practices. Chapter 5 concludes the dissertation, summarizing key findings, offering recommendations, and suggesting areas for future research.

2.      Chapter 2: Literature Review

2.1    Introduction

This chapter reviews the existing literature about the role of AI in strategic internationalisation and HR management in the context of the global expansion of Tesla. The aim is to identify key concepts and theoretical frameworks, highlighting research gaps to set up the ground for subsequent analysis.

2.2    Conceptual Framework

The conceptual framework of the study revolves around three core concepts i.e. Artificial Intelligence (AI), strategic internationalisation, and Human Resource Management (HRM). Each of these concepts is critical to understanding how Tesla leverages AI in its global expansion and HR practices.

2.2.1        Key Concepts

• Artificial Intelligence (AI): AI is the simulation of human brain intellectual processes by a machine, specifically, computer systems. These processes include learning procedures i.e. the acquisition of information and rules for using the information which, in turn, are applied in reasoning to reach approximate or definite conclusions, followed by self-correction (Haleem et al., 2022). In relation to Tesla, AI is focused on technologies such as machine learning, neural networks, and deep learning algorithms to improve vehicle safety, autonomous driving capabilities, production efficiency, and customer service.
• Strategic Internationalisation: It is the conscious attempt of the firm to expand its presence beyond the domestic market. Establishment of new geographical markets, set up of production units, product-service adaptation for the local conditions and requirements are all based on this notion (Petrou et al., 2020). For Tesla, strategic internationalisation represents Gigafactory establishment in places of relevance like Shanghai and Berlin, with an objective of capacity increase, supply chain complexities, and serving regional markets more efficiently.
• Human Resource Management (HRM): HRM is a strategic function dealing with the effective management of people in any organisation to ensure that they contribute towards attaining business objectives. HRM pertains to recruitment, training, employee engagement, performance management and succession planning (Garengo, Sardi and Nudurupati, 2021). At Tesla, AI has played a huge role in changing the organisation’s HRM practices in ways that that help in efficient recruitment, personalised training programs, enhancing employee engagement, and boosting the rates of retention.

2.2.2        Intersection of AI, Strategic Internationalisation, and HRM

Jie (2020) mentioned that it is in this nexus of AI, Strategic Internationalisation, and HRM that Tesla has been trying to persistently maintain its competitive advantage in the global market. These AI technologies help Tesla to innovate and improve operational efficiency which are two critical elements for successful internationalisation, as illustrated in Figure 3. For example, the autonomous driving technology of Tesla is not just driven by product differentiation in foreign markets but also powered by sophisticated robotics and predictive maintenance enabled by AI in the production processes. In contrast, the strategic internationalisation require an adaptive and efficient HRM approach according to Pandey et al. (2024).

As Tesla expands into new markets, the dynamics of the workforce and the regulatory environments become diversified. AI can make this process of transition easier by optimizing the HR processes slowly. This would include leveraging AI algorithms in matching job candidates with roles based on predictive analytics and ensuring the right talent is placed at the right position globally. AI in HRM also enhances development and engagement of employees (An, Lin and Luo, 2024). Such AI-driven tools will analyze employee performance data to determine training and development requirements, allow forecasts of potential employee turnover, and build up a custom career development plan.

2.3    Theoretical Framework

The theoretical framework underpinning this research includes various theories that provide clear insight into the role of AI in its strategic internationalisation and HR management practices. Some of these theories include the Resource-Based View, the Technology Acceptance Model, and Porter’s Five Forces.

2.3.1        Resource-Based View (RBV)

The Resource-Based View (RBV) is a theory of strategic management that posits that the competitive advantage of a firm resides in its internal resources. Lubis (2022) stated that according to RBV, resources that can be described as valuable, rare, inimitable, and non-substitutable (VRIN) can provide an advantage a competitive advantage.

AI is one such crucial resource for Tesla in solidifying its global competitive status. Tesla is a global market leader dealing with AI capabilities, including Full Self-Driving (FSD) and AI-driven production processes. Such AI-driven innovations will not only enhance the quality and efficiency of its products further but also lead to the addition of international markets and expansion in the existing ones (Valaei et al., 2022). Thus, the RBV framework contributes to explaining how Tesla mobilises its AI resources in its drive towards strategic internationalisation and leadership maintenance within the automotive industry.

2.3.2        Technology Acceptance Model (TAM)

Another key theory that underpins this research is the Technology Acceptance Model (TAM). TAM explains how users come to accept and use technology, emphasizing two aspects i.e. perceived usefulness and perceived ease of use (Davis, Granić and Marangunić, 2024). In regard to human resources management at Tesla Motors, TAM can be used to understand how AI technologies are adopted and used within the organisation. Therefore, AI-driven HR tools like recruitment algorithms and training platforms, must be perceived as being useful and easy to use by HR professionals and employees (Zin et al., 2023). Accordingly, this study applies a TAM theory to identify factors that influence the successful implementation of AI in HR management at Tesla, improving recruitment, training and employee engagement. 

2.3.3        Porter’s Five Forces

Porter’s Five Forces is a strategic management theory that gives a framework for the analysis of the competitive environment within which an industry operates. As shown in Figure 6, it analyses five forces that influence market competition i.e. threat of new entrants, bargaining power of suppliers, bargaining power of buyers, threat of substitute product or service, and intensity of competitive rivalry (Yang, 2022).

Porter’s Five Forces, when applied to Tesla in its global expansion, explains the external pressures and opportunities put on the firm in international markets. This is clear in the low threat of new entrants due to a very high entry barrier for the industry as the automotive business is not just capital-intensive but also needs advanced technological capabilities. On the other hand, Han (2021) showed that the bargaining power of buyers can be high as there are adequate alternatives to electric vehicles available in the market, hence, affecting Tesla’s pricing and marketing strategies. The intensity of competitive rivalry is also a major factor, since there are established makers of automobiles and new players in the fledgling electrical vehicle makers’ market. Understanding these factors, thus, enables Tesla to develop strategies meant to reduce these risks in its global expansion efforts.

2.4    AI in the Automotive Industry

Artificial intelligence has penetrated the automotive industry, changing everything from design to customer interaction. According to Bodenhausen (2022), AI applications range from autonomous driving technologies to process optimisation and better customer service. The infusion of AI into automotive operations has brought real advancement in terms of efficiency, safety, and user experience. Ahmadi et al. (2023) further added that the most well-known and earliest application of AI is in the field of autonomous driving technology. Self-driving cars, also referred to as autonomous vehicles, make decisions using AI algorithms, primarily machine learning and deep learning, on collected data from sensors and cameras that help the vehicle move around in a complicated environment. According to Montemayor and Chanda (2023), Autonomous-driving technologies have evolved from different levels of automation and driver assistance systems, such as adaptive cruise control and lane-keeping assistance.

Several case studies demonstrate exactly how AI has influenced business. For example, Toyota has integrated AI into production facilities to make operations more streamlined and productive. The company has applied AI driven robots in welding and assembly among other areas that have generally enhanced production efficiency. Similarly, Ford uses AI in the development of products through machine-learning algorithms to analyze design data and come up with optimum performance in vehicle handling (Zöldy, Szalay and Tihanyi, 2020). Case examples from Tesla, BMW, Mercedes-Benz, Toyota, and Ford illustrate how leading companies in the automobile industry have engaged in AI to shape their respective production lines to stay competitive in an evolving global market.

2.5    Tesla’s Strategic Internationalisation

GHAR (2024) illustrated that another main driver of Tesla’s evolution from a niche EV manufacturer to a global automotive powerhouse is strategic internationalisation. Historical evidence suggests that expansion at the international level occurred through the formation of a sales presence of Tesla in Europe and Asia, recognizing its rising demand for electric vehicles as indicated in Figure 8. In 2013, Tesla opened its first European assembly plant in Tilburg, Netherlands. The facility became the final assembly and distribution center of Model S and Model X cars. This was actually the initiation stage of Tesla’s serious commitment to the European market (Cooke, 2021). Lastly, in Asia, Tesla entered the Chinese market in 2014 with a very successful launch of the Model S despite regulatory hurdles and local competition.

Among the major milestones of Tesla in terms of global market penetration was the opening of Gigafactories outside the continental United States (U.S.). The Gigafactory Shanghai, started production in 2019 and was the first factory of Tesla outside the U.S. That facility is instrumental in producing Model 3 and Model Y vehicles for the Chinese market, dropping production costs and delivery times dramatically (Kuraś, 2023). Braunfels (2021) pointed out that another key milestone was the Gigafactory Berlin-Brandenburg in Germany, which is to serve the European market. The production start of this factory is planned for 2021, where it will not only manufacture batteries and battery packs but also powertrains besides the assembly of complete vehicles. These Gigafactories are strategic assets that enhance Tesla’s ability to meet regional demand efficiently (Reithmann, 2023). AI has kept Tesla at the edge in manufacturing processes, supply chain management, and in its product offering. Le and Ho (2021) stated that AI-driven automation provides a high level of precision and efficiency during vehicle production in gigafactories, thereby cutting costs while increasing output. Moreover, AI improves Tesla’s supply chain through demand prediction and inventory management which are basic building blocks of just-in-time production models that minimise storage costs and reduce waste.

2.6    AI and HR Management

Votto et al. (2021) explained that artificial intelligence in HR management is the transformation of normal traditional practices within HR to efficient, data-driven, and employee-oriented procedures. As illustrated in Figure 9, the application of AI in recruitment allows for innovative interventions in the process of hiring through automation, improvement, and innovation in candidate sourcing, screening, and selection (Huang et al., 2023).

In training and development, AI is used for enhancing learning experiences through personalised and adaptive learning platforms. AI-driven systems analyze learning patterns of employees, check their performance data, and identify gaps to develop specific training programs that meet the requirements of an individual. Examples involve AI-enabled LMS like Coursera for Business, Udemy for Business, that apply machine learning to suggest courses and training materials based on the past performance and learning preferences of an individual employee (Hmoud and Várallyai, 2020). The whole approach comes out relevant, engaging, and effective in terms of training; hence, skill development and career growth improvement. In addition, Alnamrouti, Rjoub and Ozgit (2022) elaborated that AI is central to employee engagement through tools that enhance communication, feedback, and satisfaction in the workplace. Chatbots and virtual assistants using AI are increasingly being used to facilitate employee interactions, answer queries, and provide timely support. Moreover, Wamba-Taguimdje et al. (2020) depicted that AI-driven tools can automatically perform many routine tasks like scheduling meetings, processing leave requests, and answering questions about HR policy. This frees up the time of HR professionals from managing non-core activities and gives them a chance to focus on strategic tasks. However, Khang, Jadhav and Birajdar (2023) argued that challenges also arise from the integration of AI in HR. One of the key challenge sis the potential bias in AI algorithms, thus, perpetuating existing disparities in hiring, promotions, and pay. Unless designed and monitored properly, AI systems tend to favor some groups more than others, leading to discriminatory practices inadvertently. Transparency, fairness, and accountability are very critical for any AI-driven HR process. Moreover, there are issues of data privacy and security because the practice of AI in HR is directly linked with collecting and managing huge amounts of personal and sensitive employee information. The imposition of AI into HR is a step that requires ethical vision at the outset as mentioned by Dwivedi et al. (2021). Organisations should create guidelines and best practices on how to responsibly use AI technologies toward fairness, privacy, and inclusivity. Hence, AI not only enhances HR practices through automation, personalisation, and data-driven decision-making but also brings to the forefront a number of ethical concerns with potential biases.

2.7    Link between AI, Strategic Internationalisation, and HRM

The intersection of AI, strategic internationalisation, and HRM enables areas where improvements in technology meet global business strategies and human resource practices. As per the findings of Baldegger, Caon and Sadiku (2020), AI has an enormous impact on the strategic decisions and human resource management of Tesla. As shown in Figure 10, this is achieved by the data-driven insight that enhances operational efficiency and innovates global expansion and employee management. Advanced analytics and predictive capabilities, core drivers of key strategic decisions at Tesla, are powered by AI. For example, AI-driven data analysis can help Tesla to gain information about new markets for expansion and estimate consumer demands (Zhao and Mazzalai, 2023). In its international operations, AI-powered automation and robotics in Gigafactories like Shanghai and Berlin rationalise production lines while reducing costs and improving product quality. AI transforms HR management by automating different repetitive tasks in administration and helps in better decision-making by personalisation of employee experiences.

AI-driven recruitment tools offer Tesla an efficient way for sourcing and evaluating talent from different international sources, thus, ensuring the right hirings (Kumar et al., 2024). Possible synergies between AI-driven strategies and HR management would result in more efficiency, a better focus on the realisation of the organisation’s goals, and higher employee satisfaction. However, Lampic Aaltonen and Fust (2022) showed that there are some areas of potential conflict between AI-driven strategies and HR management. First, there may be a possible incompatibility resulting from the efficiency-driven logic of AI with the human-centered approach of HRM. Even as AI optimises processes, it reduces costs and may arise employees’ fears of being substituted by machines and feelings about data privacy. These challenges must be balanced with regard to questions of transparent communication, ethics, and employee welfare (Dwivedi et al., 2021). Therefore, a complex yet synergetic relationship exists between AI integration into the strategic internationalisation and HRM of Tesla.

2.8    Literature Gap

Despite the numerous studies in areas of AI, strategic internationalisation, and HR management, gaps in literature still exist. For example, there is limited empirical analysis of the direct impact of AI on strategic internationalisation in the automotive industry particularly for companies like Tesla. Most of prior research tends to focus on AI in relation to either HR management or international strategies separately; their intersection has not been comprehensively covered. The research on impact of AI on HR practices in a global context is sparse and the ethical implications of AI-driven HR processes are understudied. Tesla’s pioneering work in AI-usage and internationalisation provides an interesting case to fill these gaps. Therefore, research questions and objectives of this study are justified to fill these gaps and provide an in-depth understanding of the transformative potential of AI in global business strategies. The study also explores Tesla’s strategies and practices, thereby contributing to the broader knowledge base about AI-driven internationalisation and human resources management. It provides practical implications for other multinational companies seeking to turn to AI for growth at the global level. Such findings can inform policymakers, business leaders, and professionals about the benefits, challenges, and ethics involved in artificial intelligence integration into international business operations and HR practices.

2.9    Chapter Summary

The chapter focuses on how AI has influenced Tesla in terms of internationalisation and HR practices. Conceptual and theoretical underpinning frameworks are analyzed in this research including the Resource-Based View, the Technology Acceptance Model, and Porter’s Five Forces. The focus will be on applications of AI in the automobile industry and human resource management highlighting how Tesla has strategically been using AI in international markets. It finally identifies the research gap, justifying the focus on Tesla and the need for further investigation of AI-driven internationalisation and the HR strategies of high-tech automotive companies.

3.      Chapter 3: Methodology

3.1    Introduction

Chapter 3 focuses on the philosophical approach, research design, methods of data collection, and techniques of data analysis. It explains the methodology’s strengths and limitations, as well as offers insight into the ethics of the study. This discussion addresses the steps taken to ensure a robust and ethically sound research process.

3.2    Philosophical Underpinnings

This research is underpinned by a relativist ontology and a constructivist epistemology that will be philosophically aligned. In terms of ontology explained by Hyde (2021), this study takes a relativist stance i.e. it recognises realities not as singular but multiple, being shaped by human experiences and perception. This is an important orientation if one wants to understand the various and complex ways through which AI influences strategic internationalisation and human resource management at Tesla.

Constructivist epistemology assumes that knowledge is not something passively received, but rather built up actively, interacting with the environment and sources of data. This study’s epistemological stance, therefore, proves to be coherent with an interest in subjective and contextual factors relating to the ways in which Tesla makes its strategic decisions and implements its HR practices. The research paradigm adopted for this research work is Interpretivism which is considered most appropriate for qualitative research. According to Mardiana (2020), interpretivism emphasises the need to understand meanings and interpretations that individuals and organisational actors ascribe to their actions and interactions. This paradigm is relevant to the study as it shows how Tesla deploys AI to drive strategic internationalisation effort and manage its workforce globally. Considering the complex interaction of AI technologies with strategic decisions and HR practices, an interpretivist approach would be important in giving detailed descriptions according to the set research objectives. By going through this paradigm, this research provided an understanding of the AI potential in global business strategies and human resource management.

3.3    Research Design

The research design of the study also adheres to the research onion framework. It will trace a path that includes all stages from the philosophical stance to data collection and analysis. On the outer layer would be the philosophical stance, which would be interpretivism, seeking an understanding of meanings behind human behavior and organisational practices. Next, at the inner layer, a qualitative research approach is adopted for the exploration of complex phenomena by detailed contextual analysis. It is an approach to understand subtle impacts of AI on the strategic internationalisation and HR management at Tesla. This approach is justified as intricate relationships and processes may not be capture by quantitative methods. Hence, existing qualitative data can be sorted to in the form of case studies, industry reports, and the current state of relevant literature to derive detailed patterns and themes on the role of AI in global expansion and HR. The qualitative approach gives in-depth understanding as to how AI influences strategic internationalisation and HR management at Tesla.

3.4    Data Collection Methods

The data collection methods in this study will be based on secondary qualitative data from various credible sources. Key sources include the academic journals, which include peer-reviewed articles and studies relevant to AI, strategic internationalisation, and HR management. Industry reports from a number of consulting firms and market research companies give insights into existing trends and practices within the automotive sector, particularly in relation to AI applications.

Information about company strategies and its operations can be derived from Tesla annual reports, press releases, and official statements. Case studies on companies like Tesla and other automobile companies have narrated elaborately how AI has been implemented and its effects on strategic internationalisation and HR management. Sources are selected based on relevance, credibility, and timelessness. According to Iovino and Tsitsianis (2020), relevance ensures that data directly addresses the research questions and objectives. Next will be the credibility of the source which shall be determined by its reputation and rigor of the methods used in data collection and analysis. Aspects of timeliness have to be considered so that it reflects the current trend and practice. These criteria aid in the selection of good sources that provide credible and relevant information to the study.

3.5    Data Analysis

Thematic Analysis used in the study is one of the tools in qualitative data analysis which helps to identify patterns within the collected data as shown by Sharma, Saha and Balaji (2022). Indeed, it is one of the most appropriate ways to find subtle insights into the role of AI in Tesla’s strategic internationalisation and human resource management. It involves coding the data, followed by systematic labeling regarding the relevance of segments of text from the secondary sources in relation to the research questions. Such codes are grouped under categories that are a reflection of broader themes pertaining to AI applications, strategic internationalisation efforts, and HR management practices. Categorisation entails structuring such themes in a coherent way that brings forth the key findings of this study. This step contributes to visualisation on how aspects of AI, strategic internationalisation, and human resource management are related or linked. The identified themes are interpreted by linking them back to the research questions and objectives. The analysis considers how the identified patterns and themes provide insight into Tesla using AI for Global Expansion and HR practices. In particular, analysing the results in relation to the existing literature and theoretical approaches provides an understanding of the transformational power of AI for international business strategies and HR management. This approach has interpretive functions, ensuring that findings are not merely descriptive but analytically rich with contributions to the research field.

3.6    Strengths and Limitations of Methodology

The strengths associated with the use of secondary data in this research are numerous. This approach is going to help in bringing out a detailed understanding of the subject matter under consideration through synthesis because it includes varied perspectives and detailed insights from various sources. However, there exist limitations to this methodology. One major limitation is the bias that may already be in secondary data, with information collected and interpreted by various different researchers with varying perspectives and aims. Moreover, it is not possible to control the quality and depth of data, which then impacts the reliability of the results. Stringent selection criteria are applied regarding relevance, credibility, and timeliness of sources to address these limitations. Moreover, triangulation is adopted to increase the robustness and validity of the analysis by comparison of findings from various sources under study.

3.7    Ethical Considerations

This research has strict adherence to ethical principles, with proper citation in respect of intellectual property and no plagiarism. The protection of data will be ensured by secure, confident handling of all secondary data. Credibility of original data sources is maintained by representing their findings accurately and acknowledgment of their contribution in finding the facts. This approach ensures the research work is in accordance with academic standards and helps to foster trustworthiness in the research findings.

3.8    Chapter Summary

This chapter focuses on the qualitative secondary research approach taken towards examining the role of AI in Tesla’s strategic internationalisation and human resource management. The explanation of an interpretivist paradigm provides a structured process for collecting and analyzing secondary data, with measures responding to strengths and limitations. Moreover, it also states adherence to ethical principles that ensure integrity and credibility for the research.

4.      Chapter 4: Analysis and Discussion

4.1    Introduction

This chapter presents the findings from the secondary qualitative research on how AI will influence Tesla’s strategic internationalisation and HR management. The intention is to analyze these findings against the literature reviewed above to address the research objectives and questions.

4.2    Thematic Analysis

4.2.1        Role of AI in Strategic Internationalisation of Tesla

The thematic analysis of the data produces various key themes and patterns that support the literature reviewed in Chapter 2. One such prominent theme is that AI had a huge impact on Tesla’s strategic internationalisation. Kumari and Bhat (2021) underscore that the most striking effects of AI is in production process optimisation and supply chain management, which has enabled Tesla to set up and run Gigafactories in Shanghai and Berlin. This is possible only due to most recent AI technologies implemented for streamlining manufacturing, reducing costs, and enhancing the quality of products. Similarly, Jiang-Min (2023) found that AI-driven automation and predictive analytics improved the efficiency of production lines while reducing downtime, allowing Tesla to meet growing global demand with ease. According to the reviewed secondary data from the various industry reports and company documents, AI-powered production at the Shanghai Gigafactory began in 2019 by monitoring and adjusting in real-time manufacturing processes (Bozesan and Bozesan, 2020; Kuraś, 2023). This has saved millions of dollars and increased production rates, providing the impetus necessary for it to become the cornerstone of Tesla’s strategy to dominate the Chinese EV market. Similarly, the Berlin Gigafactory is one of the most advanced automotive plants in the world uses AI in a variety of functions, including logistics, inventory management, and quality control. These applications enhance operational efficiency while giving Tesla a competitive edge in the European market (Cooke, 2020; GHAR, 2024). In comparison to other global automotive companies, AI-based internationalisation for Tesla appears full-fledged and more advanced.

As highlighted by Bailey et al. (2023), traditional automotive manufacturers, even while increasing their adoption of AI technologies, often have problems related to their legacy systems or slower rates of adaptation. Tesla’s innovation-driven strategy addresses all these challenges and it is well positioned to lead in AI-driven global expansion. The possible role that AI could play in optimizing the manufacturing process, supply chain management, and operational efficiency at Tesla can be understood through the initiatives of Shanghai and Berlin Gigafactories (Sypko, D., 2022; Reithmann, 2023). These findings confirm the literature assertion that AI can dramatically enhance a firm’s competitive position within the global market through cost reduction and product quality enhancement. Moreover, the data further reveals the competitive advantage Tesla gains from advanced AI integration, putting forward the notion that technological innovation is paramount for success in international expansion.

4.2.2        Impact of AI Technology on HRM in Tesla

Another key theme is AI integration into human resource management (HRM). Data suggests that because of AI-driven recruitment, training, and performance management practices at Tesla, there is heightened efficiency and better skill alignment of personnel towards the organisation’s goals (Tschang and Almirall, 2021). AI-driven HR practices revolutionised how Tesla manages its global workforce when it comes to recruitment, employee training, and performance management. Kalia and Mishra (2023) indicated that Tesla uses AI algorithms in order to make recruitment easier by screening and evaluating candidates more efficiently. The tools analyze resumes, predict the success of a candidate, and even conduct the first round of interviews with chatbots. This reduces time and resources consumed in hiring and results in a better fit for companies’ needs (KIU and TUNG, 2023; Huang et al., 2023). In the domain of training and development, it is applied to create customer learning paths for staff members, thereby filling specific gaps in their skill-sets and encouraging a continuous cycle of learning. This approach would not only develop the competence of the employee but also couple his growth processes with Tesla’s strategic goals. AI-driven platforms provide real-time feedback and adaptive learning modules so that employees learn at their pace for relevant topics.

Kumari and Bhat (2021) pointed out that AI applications increase employee engagement at Tesla as well. Sentiment analysis tools trace workers’ feedback across multiple platforms, via surveys and social media, to understand the morale background and possible problems that may arise. In such a way, Tesla acts very proactively, resolving issues in time, which keep its workers motivated. Furthermore, Girasa and Girasa (2020) showed that AI performance management systems track employee productivity and provide data-driven insights into performance trends. Therefore, these systems provide a scientific basis for managers to take promotion, reward, and development decisions. However, this adoption of AI in all human resource practices at Tesla is not without the associated challenges. Sharma, Sharma and Bansal (2023) emphasised that the major risks include ethical considerations and potential biases of AI algorithms. For example, an overreliance on historical data in training models can cause potential biases that result in unfair hiring practices or lead to biased performance appraisals (Votto et al., 2021). These risks can only be reduced by auditing continuously and refinement of AI systems within Tesla to prioritise fairness and transparency. Issues concerning data privacy have to be addressed in a manner that safeguards employee information and boosts trust (Tschang and Almirall, 2021; Hmoud and Várallyai, 2020). Therefore, AI has transformed HR management at Tesla in ways such as efficiencies, personalisation, and engagement. This finding is in agreement with literature discussing benefits from AI in HR, for example, improved candidate mapping and personalised employee development. The data, however, also points out ethical concerns and possible biases associated with AI in HR, therefore, highlighting the concern raised in the literature of continuous vigilance toward fairness of AI applications. Thus, the insights into AI-driven HR practices add depth to the understanding of how AI impacts the Human Resource Management (HRM) fulfilling the research aim of exploring the intersection between AI and HRM in a global context.

4.2.3        Opportunities and Challenges in Adopting AI in Tesla’s HRM

According to Sharma, Sharma and Bansal (2023), AI offers huge advantages in strategic internationalisation and HRM, as revealed by Tesla and other leading automobile companies. First, on the side of strategic internationalisation, AI optimises logistics, enhances market analysis, and allows for data-driven decisions. For instance, AI algorithms may scan through global market trends, consumer preferences, and competitive landscapes to advise companies on how to tailor strategies (Kalia and Mishra, 2023; Baldegger, Caon and Sadiku, 2020). AI in HRM helps streamline recruitment, training, and performance management, hence making workforce management more efficient and effective. Case studies from companies like BMW and Audi demonstrate that artificial intelligence could be very instrumental in improving production processes, cutting down costs, and increasing innovation in products. For instance, the use of AI by BMW within its supply chain has resulted in saving millions and has improved delivery time (Chimeudeonwo, 2023; Kumar et al., 2024). AI raises capacity, cuts operational expenditure, and enhances quality control at Tesla’s Gigafactories in Shanghai and Berlin. It gives examples of strategic advantages gained from the implementation of AI in increasing production capacity, reducing operational expenditure and improving product quality (Birke, Weeber and Oberle, 2022).

Although many opportunities exist as indicated in Figure 14, there are various challenges to the integration of AI with strategic internationalisation and HRM such as ethical and privacy-related issues. Kshetri (2021) argued that AI algorithms, particularly in HR practices can cause potential biases and discrimination. For instance, training based on biased data may lead to discriminative hiring decisions. Privacy issues arise from massive data collections and analyses required for AI operations, thus posing several challenges concerning data protection and consent from users (Pires et al., 2023; Lampic Aaltonen and Fust, 2022). Further constraints to the integration of AI into HR practices and international expansion stem from the potential resistance of employees, technological limitations, and large investments required in the infrastructure and training related to AI. Another avenue that is responsible for operational difficulties and problems of integration, is the complexity of AI systems (Cheryl, Ng and Wong, 2021). Strategies to mitigate these challenges are strong ethical guidelines, transparency and the auditing of AI systems to identify and correct biases. Furthermore, companies should already invest in employee training and change management programs as a means of ensuring that the smooth adoption of AI technologies is facilitated. Thus, companies must ensure a well-balanced and ethical approach towards AI integration into organisational global operations and HR practices.

4.3    Theoretical Implications

The theoretical frameworks used for the analysis of the findings are comprehensive in nature, wherein the Resource-Based View (RBV), Technology Acceptance Model (TAM), and Porter’s Five Forces are integrated. As per the study of Han (2021), Tesla’s AI capabilities act as distinctive resources providing substantial competitive advantage. According to RBV theory, the resources of any firm have to be valuable, rare, inimitable, and non-substitutable to generate a sustainable competitive advantage. Tesla’s advanced AI systems over manufacturing, autonomous driving, and finally the HR management satisfy all criteria as discussed by Wolff, Schlagwein and Schoder (2022). At Tesla, these AI capabilities allow them to drive down production cycles, increase efficiency in the supply chain, develop new products, and improve existing products. The unique integration of AI into Tesla’s operations helps in strategic positioning through improvement in operational efficiency, cost reduction, and high-quality output (Lubis, 2022). Data pertaining to the use of AI as a critical resource in expanding Tesla’s presence in the international market confirms the RBV theory, emphasizing leverage on unique resources for competitive advantage (Bredenfeld et al., 2020; Valaei et al., 2022). The findings support and complement the RBV by showing how AI enhances operational efficiency and improves market responsiveness, which has been responsible for Tesla’s global success. The research supports this theory by underscoring the very critical role that AI plays as a strategic asset in driving Tesla’s global market expansion and further strengthens its competitive positioning within the automotive industry.

The Technology Acceptance Model will be relevant in understanding the AI-driven HR practices at Tesla. In line with TAM, perceived usefulness and perceived ease of use are determinants of technology acceptance. Taking into consideration Tesla’s application of AI in its human resources function, right from recruitment to training and performance management reflects a high level of perceived usefulness since these processes simplify activities while improving employee growth (Kaur, 2020; Davis, Granić and Marangunić, 2024). The relative user-friendliness of these AI systems does guarantee that staff can effectively integrate these technologies into their daily operations. Similarly, it also points out the possible difficulties against ethical concerns and potential biases, which informs that while general acceptance may be in place for AI, fairness and transparency calls for attention over time (Wardi, 2024; Zin et al., 2023). These findings are in line with TAM and, thus, confirm the necessity of holistic strategies on the acceptance and integration of technologies in HR practice. These challenges indicate areas where TAM might expand to deal with modern AI applications in HR.

The Porter’s Five Forces framework helps in analyzing the strategic position of Tesla within the global context of the automotive industry. According to Fedotov (2022), AI has a large impact on Tesla’s competitive environment as viewed through the lens of Porter’s Five Forces framework. Through increasing production efficiency, lowering costs, and assuring quality, AI strengthens Tesla’s position against competitive rivalry. AI-driven customer service and supply chain management-related innovations also set Tesla apart from competitors, diminishing the threat of substitutes and new entrants (Yang, 2022). AI also optimises supply relationship management and buyer relationships in the quest for efficient supply chain processes and improvement of the customer experience (Lastras, 2023; Han, 2021). The findings indicate that AI reduces competitive pressures on Tesla through increased efficiency in production and high-quality products, hence building its better market position. Additionally, innovations in customer service and supply chain management by AI drive a differentiation strategy of Tesla, which aligns to Porter’s view of gaining a competitive advantage with unique capabilities.

4.4    Chapter Summary

Chapter 4 focused on the role of AI in Tesla’s strategic internationalisation and its HR management providing major implications for global market expansion and innovative workforce practices. By comparison with existing literature and theoretical framework, the discussion highlighted both the opportunities and challenges associated with AI integration. The discussion was focused on the potential of AI towards enhancing the strategic position, improving operational efficiency, and dealing with ethical dilemmas and integration complexity. These insights deepen the understanding of AI-driven strategies in the automotive industry and indicate several avenues for future research on ethical AI implementation in multinational corporations.

5.      Chapter 5: Conclusion and Recommendations

AI has turned out to be a pivotal force driving business innovation and strategic expansion across the globe. This study examines the effect of AI on Tesla’s strategic internationalisation and HR management. The objectives included an analysis of how AI influences Tesla’s strategies concerning global expansion and its HR practices, focusing mainly on the benefits and challenges of such integration. The summary of the key findings and their related implications is important in understanding the contribution that will be made by the study to theoretical and practical advancement. The study provided critical findings concerning the role of AI in Tesla’s strategic internationalisation and HR management. First, it was shown that there was a significant effect of AI on Tesla’s global market expansion, in particular through the setting up of Gigafactories in Shanghai and Berlin. AI application in production and supply management has increased its efficiency while decreasing its cost, hence making it possible for Tesla to make quick international expansion. Secondly, the AI-driven HR practices at Tesla, such as AI-based recruitment, training, employee engagement, and performance management enhances efficiency in HRM and the overall effectiveness of an organisation. Regarding contributions to existing literature, this study bridges the gap between studies on AI in business operations and those on strategic internationalisation and HR management. It also explains the ethical considerations and challenges that AI-driven HR practices face on which there is limited previous research. The research uses theories such as Resource-Based View, Technology Acceptance Model, and Porter’s Five Forces to add richness to the conceptualisation of the theoretical impact of AI on strategic internationalisation and HRM alike. The research contributes by providing practical implications for multinational corporations looking to leverage AI for global growth and operational efficiency.

5.1    Theoretical Implications

Theoretically, this study enriches the literature by enhancing the understanding on the role of AI in strategic internationalisation and in HR management. Evidence from this study supports the Resource-Based View (RBV), as it demonstrates that Tesla’s competencies in AI are unique and valuable resources underpinning its competitiveness and ability for international expansion. AI technologies applied in this result in improved operational efficiency and strategic positioning directly aligning with the RBV call to leveraging unique resources leading to sustained competitive advantage. The study also agrees with the Technology Acceptance Model (TAM) in showing how the adoption of AI in HR practices affects the acceptance and integration of AI technologies by employees. On this basis, since AI positively influences recruitment, training, and performance management at Tesla, it supports the TAM framework that perceived usefulness and perceived ease of use act as drivers of AI acceptance in the context of organisations. Furthermore, it deploys Porter’s Five Forces to explain how AI influences Tesla’s competitive environment, hence, reducing operational costs and increasing supply chain efficiency. This supports the theory as AI can affect dynamics or competitive pressures of an industry. Additionally, the study adds new theoretical contributions as it highlights some areas of ethical concerns and potential biases of AI-driven HR procedures. These insights hereby call for an expanded view of existing theories to integrate ethical dimensions and consider complexities in integrating AI into multinational corporations. The study, therefore, contributes to a deeper theoretical understanding of the transformative potential that AI holds in global business strategy.

5.2    Practical Implications

The findings from this study are also very practical and useful for Tesla and other firms that want to employ AI in their HRM and internationalisation strategies. The research shows that Tesla will probably continue improving its AI technologies to get ahead in global market competition. Through approaches such as using innovative AI-based tools in recruitment, training, and performance management, the adoption of AI in HRM could be maximised. This will ensure that the HR process works efficiently, driven by data and is enabled to identify and nurture talent globally. AI can help firms leverage their efforts towards proper market analysis, optimise supply chains, and attain efficient customer service in order to enable such firms to expand more effectively and efficiently. AI technologies that give the best employee engagement and development should, therefore, be focused on by the HR managers while addressing biasness and ethics issues. The strategic planners are devised to include AI in the long-term plan as a driver of improving efficiency and facilitating innovation. Such investment in training and up-skilling programs is to be made available for the employees to work with AI technologies. Further, creating a culture of AI-driven change that is ethical will become necessary if one wants sustainable growth and competitive advantages from the global marketplace.

5.3    Limitations of the Study

Although the research provides valuable insights, it has several limitations. First, relying on secondary qualitative data means limited control over data quality and its consistency. Indeed, while the reviewed sources were comprehensive in nature, some might have inherent biases or be outdated and therefore affect the accuracy and reliability of the findings. Secondly, since this research study has a narrow scope, it was limited to existing literature and case studies related to Tesla and AI in HRM and internationalisations. Therefore, evolving of AI technologies, along with their applicability, might not be completely captured. Furthermore, as the central focus was on Tesla, the case study approach reduced its generalisability for other organisations and industries. In the future, research may involve comparative analysis with multiple companies to give broader insight into the role of AI in strategic internationalisation and HRM. Furthermore, another limitation is that no primary data was collected which can provide more detailed insights and empirical verification of theoretical implications. Results from interviews or surveys of experts from industries or practitioners can provide understanding of the practical applications and challenges pertaining to AI within such settings. These limitations open up areas for future improvements and highlight directions into which future research can be conducted based on the findings of this study.

5.4    Recommendations for Future Research

• Future studies can use primary data like interviews, surveys, case studies with industry professionals to help gain empirical insights into the application and impact of AI utilisation within strategic internationalisation and HRM.
• Future studies could be extended to more than one company in different industries to further enhance generalisability. Comparative work consisting of different organisational contexts and implementations of AI could give wider perspectives and enable the identification of the best practices.
• Longitudinal studies that trace the trajectory of AI technologies and their consequences over time will provide the long-term implications and trends.
• Research in the area of AI ethics and associated biases as far as HRM is concerned can be undertaken in the future. One can examine how organisations can mitigate these issues and ensure that the AI-driven HR practices are fair and unbiased.
• Future studies can relate to the interaction of technologies such as machine learning, big-data analytics, or blockchain with AI in considering issues of international business and HRM.

5.5    Final Remarks

In this fast-changing global business arena, the integration of artificial intelligence has taken prime attention as a driving force for innovation and strategic growth. This project explained the transformative role of AI in strategic internationalisation and HR management at Tesla. The analysis spans from theoretical frameworks to practical implications. The findings clearly demonstrate that AI has huge potential for business strategy revolutionizing and for efficiency and competitiveness within global markets. There are a few limitations to this research; however, it serves to open avenues for exploring one of the most important parameters i.e. ethical considerations in AI integration. Ultimately, this work adds to this academic discourse and provides valuable insights that can actually be used by industry practitioners, hence marking a noteworthy step towards understanding and realizing the potential of AI within the dynamic context of international business and human resource management.

References

Ahmadi, M., Pahlavani, M., Karimi, A., Moradi, M. and Lawrence, J., 2023. The impact of the fourth industrial revolution on the transitory stage of the automotive industry. In Sustainable Manufacturing in Industry 4.0: Pathways and Practices (pp. 79-96). Singapore: Springer Nature Singapore.

Akakpo, A., Gyasi, E.A., Oduro, B. and Akpabot, S., 2019. Foresight, organization policies and management strategies in electric vehicle technology advances at tesla. Futures Thinking and Organizational Policy: Case Studies for Managing Rapid Change in Technology, Globalization and Workforce Diversity, pp.57-69.

Alnamrouti, A., Rjoub, H. and Ozgit, H., 2022. Do strategic human resources and artificial intelligence help to make organisations more sustainable? evidence from non-governmental organisations. Sustainability, 14(12), p.7327.

An, M., Lin, J. and Luo, X.R., 2024. The impact of human AI skills on organizational innovation: The moderating role of digital organizational culture. Journal of Business Research, 182, p.114786.

Bailey, D., Coffey, D., De Propris, L. and Thornley, C., 2023. The car industry as a laboratory of transformations induced by industrial development. In Handbook of Industrial Development (pp. 248-267). Edward Elgar Publishing.

Baldegger, R., Caon, M. and Sadiku, K., 2020. Correlation between entrepreneurial orientation and implementation of AI in human resources management (HRM). Technology innovation management review.

Birke, K.P., Weeber, M. and Oberle, M. eds., 2022. Handbook on smart battery cell manufacturing: the power of digitalization. World Scientific.

Bodenhausen, U., 2022, March. Make or Buy Strategy for AI in Automotive: How Much “Make-AI” is Necessary to Succeed?. In 22. Internationales Stuttgarter Symposium: Automobil-und Motorentechnik (pp. 385-396). Wiesbaden: Springer Fachmedien Wiesbaden.

Bozesan, M. and Bozesan, M., 2020. The Context of Investing. Integral Investing: From Profit to Prosperity, pp.1-95.

Braunfels, N.S.J., 2021. Tesla Motors: Disrupting the automotive industry (Master’s thesis, Universidade NOVA de Lisboa (Portugal)).

Bredenfeld, L., Cherubim, M., Kellermann, A.C., Lehmann, C., Malberg, S., Rafn, J., Kwon, Y. and Choi, S., 2020. Tesla Moving Forward. 신산업경영저널, 38(1), pp.47-70.

Cheryl, B.K., Ng, B.K. and Wong, C.Y., 2021. Governing the progress of internet-of-things: ambivalence in the quest of technology exploitation and user rights protection. Technology in Society, 64, p.101463.

Chimeudeonwo, N.B., 2023. Review on the AI technologies used in the manufacturing of electric cars (Doctoral dissertation, Technische Hochschule Ingolstadt).

Cooke, P., 2020. Gigafactory logistics in space and time: Tesla’s fourth gigafactory and its rivals. Sustainability, 12(5), p.2044.

Cooke, P., 2021. From the machine learning region to the deep learning region: Tesla, DarkTrace and DeepMind as internationalized local to global cluster firms. In The Globalization of Regional Clusters (pp. 33-57). Edward Elgar Publishing.

Cuofano, W.I.G., 2024. Tesla Business Strategy Analysis [WWW Document]. FourWeekMBA. URL https://fourweekmba.com/tesla-business-strategy/

Davis, F.D., Granić, A. and Marangunić, N., 2024. The technology acceptance model: 30 years of TAM. Springer International Publishing AG.

Du, X. and Li, B., 2021, December. Analysis of Tesla’s Marketing Strategy in China. In 2021 3rd International Conference on Economic Management and Cultural Industry (ICEMCI 2021) (pp. 1679-1687). Atlantis Press.

Dumi, 2024. Porter’s 5 Forces Framework and How to Use? – CRUD [WWW Document]. CRUD Knowledge. URL https://blog.crud.lk/porters-5-forces-framework-and-how-to-use/

Dwivedi, Y.K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A. and Galanos, V., 2021. Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International journal of information management, 57, p.101994.

Faragher, R., 2023. Tesla’s Full Self Driving Isn’t The Only Technology With Speed Sign Detection Problems. Forbes.

Fedotov, P., 2022. Critical Analysis of the Electric Vehicle Industry: Five forces and strategic action fields. Exchanges: The Interdisciplinary Research Journal, 10(1), pp.43-56.

Garengo, P., Sardi, A. and Nudurupati, S.S., 2021. Human resource management (HRM) in the performance measurement and management (PMM) domain: a bibliometric review. International Journal of Productivity and Performance Management, 71(7), pp.3056-3077.

GHAR, S., 2024. Tesla’s International Business Strategy.

Girasa, R. and Girasa, R., 2020. Applications of AI and Projections of AI Impact. Artificial Intelligence as a Disruptive Technology: Economic Transformation and Government Regulation, pp.23-67.

Haleem, A., Javaid, M., Qadri, M.A., Singh, R.P. and Suman, R., 2022. Artificial intelligence (AI) applications for marketing: A literature-based study. International Journal of Intelligent Networks, 3, pp.119-132.

Han, J., 2021. How does Tesla motors achieve competitive advantage in the global automobile industry? Journal of Next-generation Convergence Information Services Technology Vol, 10(5), pp.573-582.

Hmoud, B.I. and Várallyai, L., 2020. Artificial intelligence in human resources information systems: Investigating its trust and adoption determinants. International Journal of Engineering and Management Sciences, 5(1), pp.749-765.

Huang, X., Yang, F., Zheng, J., Feng, C. and Zhang, L., 2023. Personalized human resource management via HR analytics and artificial intelligence: Theory and implications. Asia Pacific Management Review, 28(4), pp.598-610.

Hyde, B., 2021. Critical discourse and critical reflection in Mezirow’s theory of transformative learning: A dialectic between ontology and epistemology (and a subtext of reflexivity mirroring my own onto-epistemological movement). Adult Education Quarterly, 71(4), pp.373-388.

Iovino, F. and Tsitsianis, N., 2020. The methodology of the research. In Changes in European energy markets (pp. 79-95). Emerald Publishing Limited.

Jia, Q., Guo, Y., Li, R., Li, Y., Chen, Y., 2018. A Conceptual Artificial Intelligence Application Framework in Human Resource Management.

Jiang-Min, D.I.N.G., 2023. Analyzing Tesla’s International Business Strategies: A Closer Look at the Korean and Chinese Markets. The Journal of Economics, Marketing and Management, 11(5), pp.15-27.

Jie, S., 2020. Early Internationalizing Firms’ Capabilities: Classification, configurations, and effects on international performance.

Kalia, P. and Mishra, G., 2023. Role of artificial intelligence in Re-inventing human resource management. In The Adoption and Effect of Artificial Intelligence on Human Resources Management, Part B (pp. 221-234). Emerald Publishing Limited.

Kaur, K., 2020. Future of the Workforce with the Rise of Artificial Intelligence: An Innovation Study (Doctoral dissertation, University of Southern California).

Khang, A., Jadhav, B. and Birajdar, S., 2023. Industry revolution 4.0: workforce competency models and designs. In Designing workforce management systems for industry 4.0 (pp. 11-34). CRC Press.

KIU, C. and TUNG, T., 2023. The implications and impact of artificial intelligence, big data and HR analytics in HRM: A critical analysis of EU enterprises (Doctoral dissertation, Durham University).

Kshetri, N., 2021. Cybersecurity management: An organizational and strategic approach. University of Toronto Press.

Kumar, S., Venkateshwar, A., Sharma, M. and Selladurai, S.J., 2024. Globholics-International Business: Vol. 01.

Kumari, D. and Bhat, S., 2021. Application of artificial intelligence technology in tesla-a case study. International Journal of Applied Engineering and Management Letters (IJAEML), 5(2), pp.205-218.

Kumari, D. and Bhat, S., 2021. Application of artificial intelligence technology in tesla-a case study. International Journal of Applied Engineering and Management Letters (IJAEML), 5(2), pp.205-218.

Kuraś, P., 2023. ENTRY MODES USED IN THE INTERNATIONALIZATION PROCESS OF AUTOMOTIVE ENTERPRISES. Scientific Papers of Silesian University of Technology. Organization & Management/Zeszyty Naukowe Politechniki Slaskiej. Seria Organizacji i Zarzadzanie, (180).

Lampic Aaltonen, I. and Fust, F., 2022. AI’s implications for International Entrepreneurship in the digital and pandemic world: From external and internal perspectives.

Lastras, F.S.A., 2023. A holistic approach for Business Model Innovation in the EV ecosystem: a role-based approach for analyzing BMI (Doctoral dissertation, Université Paris-Saclay).

Le, L. and Ho, Q., 2021. Factors affecting the valuation of electric vehicle company in 2020: case Tesla Inc.

Levchenko, N. and Yanchuk, T., 2024. STRATEGY FOR INCREASING THE COMPETITIVENESS OF THE TESLA ELECTRIC VEHICLE. Three Seas Economic Journal, 5(2), pp.7-13.

Liu, S., 2021, March. Competition and valuation: a case study of Tesla Motors. In IOP Conference Series: Earth and Environmental Science (Vol. 692, No. 2, p. 022103). IOP Publishing.

Lubis, N.W., 2022. Resource based view (RBV) in improving company strategic capacity. Research Horizon, 2(6), pp.587-596.

Mardiana, S., 2020. Modifying research onion for information systems research. Solid State Technology, 63(4), pp.5304-5313.

Matthews, T., Hirve, M., Pan, Y., Dang, D., Rawar, E. and Daim, T.U., 2020. Tesla Energy. Innovation Management in the Intelligent World: Cases and Tools, pp.233-249.

McCain, C., 2019. A Strategic Audit of Tesla, Inc.

Mobility, S.G., 2022. New EV entries nibbling away at Tesla EV share, according to S&P Global Mobility.

Montemayor, H.M.V. and Chanda, R.H., 2023. Automotive industry’s circularity applications and industry 4.0. Environmental Challenges, 12, p.100725.

Pandey, A., Grima, S., Pandey, S. and Balusamy, B. eds., 2024. The Role of HR in the Transforming Workplace: Challenges, Technology, and Future Directions. CRC Press.

Pereira, G.G., 2021. The Impact of Human Resource Management on Eemployee’s Performance and Life Satisfaction During Crisis Situations (Master’s thesis, Universidade do Porto (Portugal)).

Petrou, A.P., Hadjielias, E., Thanos, I.C. and Dimitratos, P., 2020. Strategic decision-making processes, international environmental munificence and the accelerated internationalization of SMEs. International Business Review, 29(5), p.101735.

Phan, K. and Daim, T.U., 2011. Exploring technology acceptance for mobile services. Journal of Industrial Engineering and Management (JIEM), 4(2), pp.339-360.

Pires, P.B., Santos, J.D., Pereira, I.V. and Torres, A.I. eds., 2023. Confronting security and privacy challenges in digital marketing. IGI Global.

Reithmann, T., 2023. Giga-Sense: The effect of place transformation on residents’ sense of place.

Rishi, M., Dwivedi, P. and Ghosal, A., 2022. Application of RBV Theory in Business Management-A Bibliometric Analysis. Acta Universitatis Bohemiae Meridionales, 25(2).

Saxena, N. and Vibhandik, S., 2021. Tesla’s Competitive Strategies and Emerging Markets Challenges. IUP Journal of Brand Management, 18(3).

Shao, X., Wang, Q. and Yang, H., 2021. Business Analysis and Future Development of an Electric Vehicle Company–Tesla.

Sharma, A., Sharma, D. and Bansal, R., 2023. Challenges of adopting artificial technology in human resource management practices. In The Adoption and Effect of Artificial Intelligence on Human Resources Management, Part B (pp. 111-126). Emerald Publishing Limited.

Sharma, P., Saha, S. and Balaji, M.S., 2022. Retrospective view and thematic analysis of business-to-business relationships through bibliometric analysis. Journal of Business-To-Business Marketing, 29(1), pp.19-42.

Shoeb, S., 2023. Tesla’s Marketing Strategy [WWW Document]. URL https://www.linkedin.com/pulse/teslas-marketing-strategy-shaikh-shoeb

Sypko, D., 2022. Time-Based Competition in Tesla´ s Supply Chain in the Era of Industry 4.0.

Thomas, V.J. and Maine, E., 2019. Market entry strategies for electric vehicle start-ups in the automotive industry–Lessons from Tesla Motors. Journal of Cleaner Production, 235, pp.653-663.

Tschang, F.T. and Almirall, E., 2021. Artificial intelligence as augmenting automation: Implications for employment. Academy of Management Perspectives, 35(4), pp.642-659.

Valaei, N., Rezaei, S., Bressolles, G. and Dent, M.M., 2022. Indispensable components of creativity, innovation, and FMCG companies’ competitive performance: a resource-based view (RBV) of the firm. Asia-Pacific Journal of Business Administration, 14(1), pp.1-26.

Verlinden, N., 2023. AI in HR: 2024 Guide to Opportunities and Applications in HR [WWW Document]. AIHR. URL https://www.aihr.com/blog/ai-in-hr/

Votto, A.M., Valecha, R., Najafirad, P. and Rao, H.R., 2021. Artificial intelligence in tactical human resource management: A systematic literature review. International Journal of Information Management Data Insights, 1(2), p.100047.

Wamba-Taguimdje, S.L., Wamba, S.F., Kamdjoug, J.R.K. and Wanko, C.E.T., 2020. Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business process management journal, 26(7), pp.1893-1924.

Wardi, S., 2024. Exploring the role of artificial intelligence in project management (Master’s thesis, Universitat Politècnica de Catalunya).

Wolff, B., Schlagwein, D. and Schoder, D., 2022. How Resource Openness Supports Industrial Transformation and Competitive Advantage: The Revelatory Case of Tesla. In Pacific Asia Conference on Information Systems (p. 1).

Yang, X., 2022, July. Research on Tesla’s Market—Based on Porter’s Five Forces and Ratio Analysis Model. In 2022 2nd International Conference on Enterprise Management and Economic Development (ICEMED 2022) (pp. 773-777). Atlantis Press.

Zaki, M.A.A.R., 2017. Implementing customer relationship management in governmental organizations.

Zhao, Z. and Mazzalai, A., 2023. How do firms acquire strategic human resources for facilitating innovation-A multiple-case study of MNCs which have headquarters in European context.

Zin, K.S.L.T., Kim, S., Kim, H.S. and Feyissa, I.F., 2023. A study on technology acceptance of digital healthcare among older Korean adults using extended tam (extended technology acceptance model). Administrative Sciences, 13(2), p.42.

Zöldy, M., Szalay, Z. and Tihanyi, V., 2020. Challenges in homologation process of vehicles with artificial intelligence. Transport, 35(4), pp.447-453.